Processing facilities are often managed using industrial process control and automation systems. These types of systems routinely include sensors, actuators, and process controllers. Some of the process controllers typically receive measurements from the sensors and generate control signals for the actuators. Other controllers often perform higher-level functions, such as planning, scheduling, and optimization operations.
Some controllers in an industrial process control and automation system may require tuning or adjustment from time to time. For example, some controllers can implement model predictive control (MPC) or other model-based control techniques, which use models mathematically representing how industrial processes behave in response to changes to their inputs in order to control the industrial processes. As another example, some controllers can implement proportional-integral-derivative (PID) control techniques, which use feedback to identify errors between process variables in industrial processes and desired values in order to minimize the errors over time. If an industrial process changes or a model or PID control loop cannot control the industrial process with enough accuracy, the model or control loop may need to be updated or replaced.